High-dimensional generalized linear models
Web3 de fev. de 2024 · Variable selection in a grouped manner is an attractive method since it respects the grouping structure in the data. In this paper, we study the adaptive group Lasso in the frame of high-dimensional generalized linear models. Both the number of groups diverging with the sample size and the number of groups exceeding the sample … Web1 de jul. de 2024 · Many current intrinsically interpretable machine learning models can only handle the data that are linear, low-dimensional, and relatively independent attributes and often with discrete attribute values, while the models that are capable of handling high-dimensional nonlinear data, like deep learning, have very poor interpretability.
High-dimensional generalized linear models
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Webthe high-dimensional GLM transfer learning setting. Extensive simulations and a real-data experiment verify the e ectiveness of our algorithms. Keywords: Generalized linear … WebIn this work, we study the transfer learning problem under high-dimensional generalized linear models (GLMs), which aim to improve the fit on target data by borrowing …
http://www-stat.wharton.upenn.edu/~tcai/paper/html/Transfer-Learning-GLM.html Webmethods for transfer learning in high-dimensional linear models and establishes the mini-max optimal rate.Li et al.(2024b) introduces a method for estimation and edge detection …
Web1 de set. de 2015 · Variable selection for high-dimensional generalized linear models with the weighted elastic-net procedure September 2015 Journal of Applied Statistics 43(5):1-14 WebTony Cai, Zijian Guo, and Rong Ma. Abstract: This paper develops a unified statistical inference framework for high-dimensional binary generalized linear models (GLMs) with general link functions. Both unknown and known design distribution settings are considered. A two-step weighted bias-correction method is proposed for constructing ...
Web12 de fev. de 2024 · High-dimensional Generalized Linear Model (GLM) inferences have been studied by many scholars [3,4,5,6]. Deshpande proposed a debiasing method for constructing CIs. Cai, Athey and Zhu [8,9,10] proposed a more general linear comparison method under the condition of special load vectors.
http://www-stat.wharton.upenn.edu/~tcai/paper/html/Inference-GLM.html fluorescent bulb ballast overheatWeb1 de out. de 2024 · In this paper, we propose to use a penalized estimator for the homogeneity detection in the high-dimensional generalized linear model (GLM), that composed of two non-convex penalties: individual sparsity and sparsity of pairwise difference. We consider a class of non-convex penalties that includes most of existing … greenfield ia movie theaterWeb1 de out. de 2024 · In this paper, we propose to use a penalized estimator for the homogeneity detection in the high-dimensional generalized linear model (GLM), that … greenfield ignitors softball tryouts 2022WebHigh-dimensional data and linear models: a review M Brimacombe Department of Biostatistics, University of Kansas Medical Center, Kansas City, KS, USA Abstract: The … greenfield ignitors fastpitch softballWebThis study proposes a novel complete subset averaging (CSA) method for high-dimensional generalized linear models based on a penalized Kullback–Leibler (KL) loss. All models under consideration can be potentially misspecified, and the dimension of covariates is allowed to diverge to infinity. fluorescent bulb conversionWebWe consider high-dimensional generalized linear models with Lipschitz loss functions, and prove a nonasymptotic oracle inequality for the empirical risk minimizer with Lasso … fluorescent bulb conversion chartWeb20 de fev. de 2014 · We consider testing regression coefficients in high dimensional generalized linear models. An investigation of the test of Goeman et al. (2011) is … fluorescent bulb burns out immediately